End Accuracy \(\mathbb {E}\).
We compare our full model (SDAF) with respect to several state-of-the-art baselines, including EWC++ [1]}, ER [2]}, AGEM [3]}, GSS [4]}, MIR [5]}, ASER [6]}, DualNet [7]}, DVC [8]}, SCR [9]}, and SCL [10]}, in which the corresponding explanations of these methods have been provided in Appendix REF .
As our proposed method multiplies the size of a batch by 8 times (i.e. firstly adopting semantically distinct augmentation to augment an image into \(K=4\)  instances, followed by generating 2 views for each of the instances),
in order to have the fair comparison among our proposed method and the baselines in terms of the same computational cost,
we set the number of SGD update \(I\)  to 1 for our proposed method, \(I\)  to 4 for the baselines based on contrastive learning (e.g. SCR, SCL, DualNet, and DVC), and \(I\)  to 8 for the other methods.
Please note that, as DualNet contains two components (i.e. a slow learner adopting self-supervised learning and a fast learner adopting supervised learning), both of these two components will run for \(I=4\)  SGD updates before receiving the next new batch. We follow the aforementioned settings of \(I\)  for SGD updates in all of our experiments unless otherwise
specified. The results in terms of end accuracy \(\mathbb {E}\)  is shown in Table REF , in which it is clear to observe that our proposed SDAF method outperforms almost of the baselines on different datasets with various settings of memory size \(\mathcal {M}\)  except being slightly worse than DVC on Mini-ImageNet with \(\mathcal {M}=5000\)  (noting that DVC has a specific strategy to draw the most informative samples from memory, where such strategy benefits more when memory size gets larger).
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